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You searched for +publisher:"University of Cincinnati" +contributor:("Sivaganesan, Siva"). Showing records 1 – 14 of 14 total matches.

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University of Cincinnati

1. Glore, Mary Lee. The Threshold Prior in Bayesian Hypothesis Testing.

Degree: PhD, Arts and Sciences: Mathematics (Statistics), 2014, University of Cincinnati

 Bayesian hypothesis testing provides an attractive alternative that overcomes the difficulties with interpreting the p-value or calibrating it. Bayesian hypothesis testing also provides measures of… (more)

Subjects/Keywords: Statistics; Bayes; regression; Mathematica; nonlocal; g-prior; time series

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APA (6th Edition):

Glore, M. L. (2014). The Threshold Prior in Bayesian Hypothesis Testing. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1416570546

Chicago Manual of Style (16th Edition):

Glore, Mary Lee. “The Threshold Prior in Bayesian Hypothesis Testing.” 2014. Doctoral Dissertation, University of Cincinnati. Accessed August 06, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1416570546.

MLA Handbook (7th Edition):

Glore, Mary Lee. “The Threshold Prior in Bayesian Hypothesis Testing.” 2014. Web. 06 Aug 2020.

Vancouver:

Glore ML. The Threshold Prior in Bayesian Hypothesis Testing. [Internet] [Doctoral dissertation]. University of Cincinnati; 2014. [cited 2020 Aug 06]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1416570546.

Council of Science Editors:

Glore ML. The Threshold Prior in Bayesian Hypothesis Testing. [Doctoral Dissertation]. University of Cincinnati; 2014. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1416570546


University of Cincinnati

2. Xiao, Yang. A Bayesian Subgroup Analysis Using An Additive Model.

Degree: PhD, Arts and Sciences: Mathematical Sciences, 2013, University of Cincinnati

 In clinical trials, subgroup analysis refers to the experimental design and statistical analysisto investigate the efficacy of a new drug in patient groups defined by… (more)

Subjects/Keywords: Statistics; Bayesian subgroup analysis; Bayesian model selction; additive model

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APA (6th Edition):

Xiao, Y. (2013). A Bayesian Subgroup Analysis Using An Additive Model. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1384870215

Chicago Manual of Style (16th Edition):

Xiao, Yang. “A Bayesian Subgroup Analysis Using An Additive Model.” 2013. Doctoral Dissertation, University of Cincinnati. Accessed August 06, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1384870215.

MLA Handbook (7th Edition):

Xiao, Yang. “A Bayesian Subgroup Analysis Using An Additive Model.” 2013. Web. 06 Aug 2020.

Vancouver:

Xiao Y. A Bayesian Subgroup Analysis Using An Additive Model. [Internet] [Doctoral dissertation]. University of Cincinnati; 2013. [cited 2020 Aug 06]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1384870215.

Council of Science Editors:

Xiao Y. A Bayesian Subgroup Analysis Using An Additive Model. [Doctoral Dissertation]. University of Cincinnati; 2013. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1384870215


University of Cincinnati

3. Lin, Min. Correlation of Bivariate Frailty Models and a New Marginal Weibull Distribution for Correlated Bivariate Survival Data.

Degree: PhD, Arts and Sciences: Mathematical Sciences, 2011, University of Cincinnati

 Survival analysis is widely used in many different areas. The classic models, such as Cox proportional hazards model, are frequently used to model univariate survival… (more)

Subjects/Keywords: Statistics; frailty model; correlation; bivariate marginal Weibull; loglinear survival model; Bayesian methods

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APA (6th Edition):

Lin, M. (2011). Correlation of Bivariate Frailty Models and a New Marginal Weibull Distribution for Correlated Bivariate Survival Data. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1307321226

Chicago Manual of Style (16th Edition):

Lin, Min. “Correlation of Bivariate Frailty Models and a New Marginal Weibull Distribution for Correlated Bivariate Survival Data.” 2011. Doctoral Dissertation, University of Cincinnati. Accessed August 06, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1307321226.

MLA Handbook (7th Edition):

Lin, Min. “Correlation of Bivariate Frailty Models and a New Marginal Weibull Distribution for Correlated Bivariate Survival Data.” 2011. Web. 06 Aug 2020.

Vancouver:

Lin M. Correlation of Bivariate Frailty Models and a New Marginal Weibull Distribution for Correlated Bivariate Survival Data. [Internet] [Doctoral dissertation]. University of Cincinnati; 2011. [cited 2020 Aug 06]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1307321226.

Council of Science Editors:

Lin M. Correlation of Bivariate Frailty Models and a New Marginal Weibull Distribution for Correlated Bivariate Survival Data. [Doctoral Dissertation]. University of Cincinnati; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1307321226


University of Cincinnati

4. Ding, Lili. Bayesian Frailty Models for Correlated Interval-Censored Survival Data.

Degree: PhD, Arts and Sciences : Mathematical Sciences, 2010, University of Cincinnati

 Interval-censored time to event data occur in survival analysis when the event time is only known to fall into an interval and these intervals often… (more)

Subjects/Keywords: Statistics; Bayesian; Frailty; Interval-censored; Puberty

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APA (6th Edition):

Ding, L. (2010). Bayesian Frailty Models for Correlated Interval-Censored Survival Data. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1267454031

Chicago Manual of Style (16th Edition):

Ding, Lili. “Bayesian Frailty Models for Correlated Interval-Censored Survival Data.” 2010. Doctoral Dissertation, University of Cincinnati. Accessed August 06, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1267454031.

MLA Handbook (7th Edition):

Ding, Lili. “Bayesian Frailty Models for Correlated Interval-Censored Survival Data.” 2010. Web. 06 Aug 2020.

Vancouver:

Ding L. Bayesian Frailty Models for Correlated Interval-Censored Survival Data. [Internet] [Doctoral dissertation]. University of Cincinnati; 2010. [cited 2020 Aug 06]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1267454031.

Council of Science Editors:

Ding L. Bayesian Frailty Models for Correlated Interval-Censored Survival Data. [Doctoral Dissertation]. University of Cincinnati; 2010. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1267454031


University of Cincinnati

5. Li, Dandan. On Multiplicity Adjustment in Bayesian Variable Selection and An Objective Bayesian Analysis of a Crossover Design.

Degree: PhD, Arts and Sciences: Mathematical Sciences, 2014, University of Cincinnati

 Multiplicity is a common problem in multiple testing for both traditional and Bayesian approaches. The first part of this dissertation considers certain issues relating to… (more)

Subjects/Keywords: Statistics; multiplicity adjustment; variable selection; objective Bayesian; crossover design

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APA (6th Edition):

Li, D. (2014). On Multiplicity Adjustment in Bayesian Variable Selection and An Objective Bayesian Analysis of a Crossover Design. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1396453837

Chicago Manual of Style (16th Edition):

Li, Dandan. “On Multiplicity Adjustment in Bayesian Variable Selection and An Objective Bayesian Analysis of a Crossover Design.” 2014. Doctoral Dissertation, University of Cincinnati. Accessed August 06, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1396453837.

MLA Handbook (7th Edition):

Li, Dandan. “On Multiplicity Adjustment in Bayesian Variable Selection and An Objective Bayesian Analysis of a Crossover Design.” 2014. Web. 06 Aug 2020.

Vancouver:

Li D. On Multiplicity Adjustment in Bayesian Variable Selection and An Objective Bayesian Analysis of a Crossover Design. [Internet] [Doctoral dissertation]. University of Cincinnati; 2014. [cited 2020 Aug 06]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1396453837.

Council of Science Editors:

Li D. On Multiplicity Adjustment in Bayesian Variable Selection and An Objective Bayesian Analysis of a Crossover Design. [Doctoral Dissertation]. University of Cincinnati; 2014. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1396453837


University of Cincinnati

6. Shen, Rui. A Bayesian Modeling of Monotonic Ordinal Responses with Application to Maturation.

Degree: PhD, Arts and Sciences : Mathematical Sciences, 2009, University of Cincinnati

 In this dissertation, A bayesian approach was used to model multinomial ordinal outcomes. Responses without and with misclassication were considered. Transition probability with possible dependence… (more)

Subjects/Keywords: Statistics; monotonic ordinal response; Bayesian; maturation

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APA (6th Edition):

Shen, R. (2009). A Bayesian Modeling of Monotonic Ordinal Responses with Application to Maturation. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1258725412

Chicago Manual of Style (16th Edition):

Shen, Rui. “A Bayesian Modeling of Monotonic Ordinal Responses with Application to Maturation.” 2009. Doctoral Dissertation, University of Cincinnati. Accessed August 06, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1258725412.

MLA Handbook (7th Edition):

Shen, Rui. “A Bayesian Modeling of Monotonic Ordinal Responses with Application to Maturation.” 2009. Web. 06 Aug 2020.

Vancouver:

Shen R. A Bayesian Modeling of Monotonic Ordinal Responses with Application to Maturation. [Internet] [Doctoral dissertation]. University of Cincinnati; 2009. [cited 2020 Aug 06]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1258725412.

Council of Science Editors:

Shen R. A Bayesian Modeling of Monotonic Ordinal Responses with Application to Maturation. [Doctoral Dissertation]. University of Cincinnati; 2009. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1258725412

7. Meng, Xiangxiang. Spectral Bayesian Network and Spectral Connectivity Analysis for Functional Magnetic Resonance Imaging Studies.

Degree: PhD, Arts and Sciences: Mathematical Sciences, 2011, University of Cincinnati

  Narrative comprehension is a fundamental cognitive skill that involves the coordination of different functional brain regions. To investigate the network structure among the brain… (more)

Subjects/Keywords: Statistics; Bayesian Network; Bayesian Model Averaging; Spectral Density Matrix; Spectral Connectivity; Bootstrap; functional Magnetic Resonance Imaging

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APA (6th Edition):

Meng, X. (2011). Spectral Bayesian Network and Spectral Connectivity Analysis for Functional Magnetic Resonance Imaging Studies. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1320681959

Chicago Manual of Style (16th Edition):

Meng, Xiangxiang. “Spectral Bayesian Network and Spectral Connectivity Analysis for Functional Magnetic Resonance Imaging Studies.” 2011. Doctoral Dissertation, University of Cincinnati. Accessed August 06, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1320681959.

MLA Handbook (7th Edition):

Meng, Xiangxiang. “Spectral Bayesian Network and Spectral Connectivity Analysis for Functional Magnetic Resonance Imaging Studies.” 2011. Web. 06 Aug 2020.

Vancouver:

Meng X. Spectral Bayesian Network and Spectral Connectivity Analysis for Functional Magnetic Resonance Imaging Studies. [Internet] [Doctoral dissertation]. University of Cincinnati; 2011. [cited 2020 Aug 06]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1320681959.

Council of Science Editors:

Meng X. Spectral Bayesian Network and Spectral Connectivity Analysis for Functional Magnetic Resonance Imaging Studies. [Doctoral Dissertation]. University of Cincinnati; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1320681959

8. Zou, Yuanshu. Robustifying a Non-Linear Model using Wavelets: A Bayesian Approach with an Application to Pharmacokinetics Modeling.

Degree: PhD, Arts and Sciences: Mathematical Sciences, 2013, University of Cincinnati

 Parametric models are easy to fit and easy to interpret, however, they may not be sufficientfor modeling data arising from complex phenomenon. Nonparametric models allowflexible… (more)

Subjects/Keywords: Statistics; robustify; wavelet; bayesian; pharmacokinetics

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APA (6th Edition):

Zou, Y. (2013). Robustifying a Non-Linear Model using Wavelets: A Bayesian Approach with an Application to Pharmacokinetics Modeling. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378197606

Chicago Manual of Style (16th Edition):

Zou, Yuanshu. “Robustifying a Non-Linear Model using Wavelets: A Bayesian Approach with an Application to Pharmacokinetics Modeling.” 2013. Doctoral Dissertation, University of Cincinnati. Accessed August 06, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378197606.

MLA Handbook (7th Edition):

Zou, Yuanshu. “Robustifying a Non-Linear Model using Wavelets: A Bayesian Approach with an Application to Pharmacokinetics Modeling.” 2013. Web. 06 Aug 2020.

Vancouver:

Zou Y. Robustifying a Non-Linear Model using Wavelets: A Bayesian Approach with an Application to Pharmacokinetics Modeling. [Internet] [Doctoral dissertation]. University of Cincinnati; 2013. [cited 2020 Aug 06]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378197606.

Council of Science Editors:

Zou Y. Robustifying a Non-Linear Model using Wavelets: A Bayesian Approach with an Application to Pharmacokinetics Modeling. [Doctoral Dissertation]. University of Cincinnati; 2013. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378197606

9. Guo, Yixuan. Bayesian Model Selection for Poisson and Related Models.

Degree: PhD, Arts and Sciences: Mathematical Sciences, 2015, University of Cincinnati

 Poisson model is one of the fundamental discrete models used to model count data in various fields. It assumes that the mean and variance of… (more)

Subjects/Keywords: Statistics; Model selection; Posterior probability; Jeffreys prior; Generalized Poisson model; Zero-inflated Poisson model; Zero-inflated generalized Poisson model

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APA (6th Edition):

Guo, Y. (2015). Bayesian Model Selection for Poisson and Related Models. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439310177

Chicago Manual of Style (16th Edition):

Guo, Yixuan. “Bayesian Model Selection for Poisson and Related Models.” 2015. Doctoral Dissertation, University of Cincinnati. Accessed August 06, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439310177.

MLA Handbook (7th Edition):

Guo, Yixuan. “Bayesian Model Selection for Poisson and Related Models.” 2015. Web. 06 Aug 2020.

Vancouver:

Guo Y. Bayesian Model Selection for Poisson and Related Models. [Internet] [Doctoral dissertation]. University of Cincinnati; 2015. [cited 2020 Aug 06]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439310177.

Council of Science Editors:

Guo Y. Bayesian Model Selection for Poisson and Related Models. [Doctoral Dissertation]. University of Cincinnati; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439310177

10. Li, Qian. Approaches to Find the Functionally Related Experiments Based on Enrichment Scores: Infinite Mixture Model Based Cluster Analysis for Gene Expression Data.

Degree: PhD, Arts and Sciences: Mathematical Sciences, 2013, University of Cincinnati

 DNA microarray is a widely used high-throughput technology to measure theexpression level of tens of thousands of genes simultaneously. With increasingavailability of microarray genomics data,… (more)

Subjects/Keywords: Statistics; Microarray data; Clustering Analysis; Dirichlet process mixture model; Infinite Mixture model; Enrichment score; Functional category

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APA (6th Edition):

Li, Q. (2013). Approaches to Find the Functionally Related Experiments Based on Enrichment Scores: Infinite Mixture Model Based Cluster Analysis for Gene Expression Data. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378113351

Chicago Manual of Style (16th Edition):

Li, Qian. “Approaches to Find the Functionally Related Experiments Based on Enrichment Scores: Infinite Mixture Model Based Cluster Analysis for Gene Expression Data.” 2013. Doctoral Dissertation, University of Cincinnati. Accessed August 06, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378113351.

MLA Handbook (7th Edition):

Li, Qian. “Approaches to Find the Functionally Related Experiments Based on Enrichment Scores: Infinite Mixture Model Based Cluster Analysis for Gene Expression Data.” 2013. Web. 06 Aug 2020.

Vancouver:

Li Q. Approaches to Find the Functionally Related Experiments Based on Enrichment Scores: Infinite Mixture Model Based Cluster Analysis for Gene Expression Data. [Internet] [Doctoral dissertation]. University of Cincinnati; 2013. [cited 2020 Aug 06]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378113351.

Council of Science Editors:

Li Q. Approaches to Find the Functionally Related Experiments Based on Enrichment Scores: Infinite Mixture Model Based Cluster Analysis for Gene Expression Data. [Doctoral Dissertation]. University of Cincinnati; 2013. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378113351

11. Ren, Yan. A Non-parametric Bayesian Method for Hierarchical Clustering of Longitudinal Data.

Degree: PhD, Arts and Sciences: Mathematical Sciences, 2012, University of Cincinnati

 In longitudinal studies, we are often interested in simultaneously clustering observations at both subject- and time-levels. Current clustering approaches assume the exchangeability among clustering units,… (more)

Subjects/Keywords: Statistics; cluster analysis; Bayesian; longitudinal data; Dirichlet process mixture (DPM) model; reversible jump MCMC; Gibbs

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APA (6th Edition):

Ren, Y. (2012). A Non-parametric Bayesian Method for Hierarchical Clustering of Longitudinal Data. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1337085531

Chicago Manual of Style (16th Edition):

Ren, Yan. “A Non-parametric Bayesian Method for Hierarchical Clustering of Longitudinal Data.” 2012. Doctoral Dissertation, University of Cincinnati. Accessed August 06, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1337085531.

MLA Handbook (7th Edition):

Ren, Yan. “A Non-parametric Bayesian Method for Hierarchical Clustering of Longitudinal Data.” 2012. Web. 06 Aug 2020.

Vancouver:

Ren Y. A Non-parametric Bayesian Method for Hierarchical Clustering of Longitudinal Data. [Internet] [Doctoral dissertation]. University of Cincinnati; 2012. [cited 2020 Aug 06]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1337085531.

Council of Science Editors:

Ren Y. A Non-parametric Bayesian Method for Hierarchical Clustering of Longitudinal Data. [Doctoral Dissertation]. University of Cincinnati; 2012. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1337085531

12. Li, Xia. A Bayesian Hierarchical Model for Studying Inter-Occasion and Inter-Subject Variability in Pharmacokinetics.

Degree: PhD, Arts and Sciences: Mathematical Sciences, 2011, University of Cincinnati

  This dissertation includes two parts: developing a new model for individual pharmacokinetics (PK) and applying a Bayesian three-stage hierarchical model to population PK. As… (more)

Subjects/Keywords: Statistics; Bayesian three-stage hierarchical model; population pharmacokinetics; Interoccasion variability; compartment modeling; piecewise absorption; Gibbs sampling and Metropolis-Hasting

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APA (6th Edition):

Li, X. (2011). A Bayesian Hierarchical Model for Studying Inter-Occasion and Inter-Subject Variability in Pharmacokinetics. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1296592410

Chicago Manual of Style (16th Edition):

Li, Xia. “A Bayesian Hierarchical Model for Studying Inter-Occasion and Inter-Subject Variability in Pharmacokinetics.” 2011. Doctoral Dissertation, University of Cincinnati. Accessed August 06, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1296592410.

MLA Handbook (7th Edition):

Li, Xia. “A Bayesian Hierarchical Model for Studying Inter-Occasion and Inter-Subject Variability in Pharmacokinetics.” 2011. Web. 06 Aug 2020.

Vancouver:

Li X. A Bayesian Hierarchical Model for Studying Inter-Occasion and Inter-Subject Variability in Pharmacokinetics. [Internet] [Doctoral dissertation]. University of Cincinnati; 2011. [cited 2020 Aug 06]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1296592410.

Council of Science Editors:

Li X. A Bayesian Hierarchical Model for Studying Inter-Occasion and Inter-Subject Variability in Pharmacokinetics. [Doctoral Dissertation]. University of Cincinnati; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1296592410

13. Chen, Chen. Bayesian Analyses of Mediational Models for Survival Outcome.

Degree: PhD, Arts and Sciences: Mathematical Sciences, 2011, University of Cincinnati

 This dissertation focuses on Bayesian mediation analysis for survival outcome. It consistsof three parts. The first part of this dissertation is Bayesian semi-parametric mediationanalysis for… (more)

Subjects/Keywords: Mathematics; Bayesian; Mediation; Survival Outcome; Incomplete; longitudinal

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APA (6th Edition):

Chen, C. (2011). Bayesian Analyses of Mediational Models for Survival Outcome. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1313684054

Chicago Manual of Style (16th Edition):

Chen, Chen. “Bayesian Analyses of Mediational Models for Survival Outcome.” 2011. Doctoral Dissertation, University of Cincinnati. Accessed August 06, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1313684054.

MLA Handbook (7th Edition):

Chen, Chen. “Bayesian Analyses of Mediational Models for Survival Outcome.” 2011. Web. 06 Aug 2020.

Vancouver:

Chen C. Bayesian Analyses of Mediational Models for Survival Outcome. [Internet] [Doctoral dissertation]. University of Cincinnati; 2011. [cited 2020 Aug 06]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1313684054.

Council of Science Editors:

Chen C. Bayesian Analyses of Mediational Models for Survival Outcome. [Doctoral Dissertation]. University of Cincinnati; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1313684054

14. Liu, Jinzhong. Bayesian Inference for Treatment Effect.

Degree: PhD, Arts and Sciences: Mathematical Sciences, 2017, University of Cincinnati

 Evaluation of overall treatment effect and heterogeneity in treatment effect is of interest in both randomized clinical trials and in observational studies. In this thesis,… (more)

Subjects/Keywords: Statistics; Bayesian; Subgroup analysis; Causal inference; Gaussian process; Selection bias

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APA (6th Edition):

Liu, J. (2017). Bayesian Inference for Treatment Effect. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504803668961964

Chicago Manual of Style (16th Edition):

Liu, Jinzhong. “Bayesian Inference for Treatment Effect.” 2017. Doctoral Dissertation, University of Cincinnati. Accessed August 06, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504803668961964.

MLA Handbook (7th Edition):

Liu, Jinzhong. “Bayesian Inference for Treatment Effect.” 2017. Web. 06 Aug 2020.

Vancouver:

Liu J. Bayesian Inference for Treatment Effect. [Internet] [Doctoral dissertation]. University of Cincinnati; 2017. [cited 2020 Aug 06]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504803668961964.

Council of Science Editors:

Liu J. Bayesian Inference for Treatment Effect. [Doctoral Dissertation]. University of Cincinnati; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504803668961964

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